5 Things Data Cannot Do

5 Things Data Cannot Do
Data Will Help Us | Jonathan Harris
Data Will Help Us | Jonathan Harris

Johanna Drucker’s talk at Concordia in the Fall 2015 left me thinking about all the things that those of us who work with digital tools ask of data. This inspired me to consider the limits of what data can do, which is the topic of this article. A simple Google search locates a plethora of articles on this very topic, which is perhaps indicative of the general skepticism of Big Data. Generally speaking, to illuminate what data can do and how we can effectively employ big data in our research, we need to address its limits.

1. Data cannot function as testimony.

Writing for the Arclight website, in “Teaching with Arclight and POE” Eric Hoyt illustrates how Arclight can be employed in a film or media history classroom. For example, Arclight can be used in the first step in the POE (Predict, Observe, Explain) strategy. Hoyt explains:

Arclight offers one means of integrating POE and active learning into a film or media history classroom. To use my earlier graph example, a teacher might ask, “How did the discourse of sports change from 1900 to 1960 in books and magazines about American entertainment and media?” Students could write down their predictions, then get to work on their computers or phones running queries for baseball, basketball, football, and other terms in Arclight. Something might immediately jump out at them. For me, it was the decline of both baseball and football during the years of 1942, 1943, 1944, and 1945. Based on this observation, I would offer the explanation that this decline of baseball and   football occurred due to the impact of World War II and the enlistment of athletes into armed forces. (para. 6)

Approached in this way, Hoyt contends, “distant reading is a new prediction that invites closer inspection, observation, and analysis.” Thus, the graphs produced through Arclight are visualizations and predictions that require further exploration, and not, in fact, evidence or proof of a specific phenomenon. As Digital Archivist Trevor Owens asserts, data is “not in and of itself a kind of evidence but a multifaceted object which can be mobilized as evidence in support of an argument” (para. 2). Thus, it is vital to remember that “visualizations are always interpretations” of data, not mere snapshots (Drucker, Graphesis 7, 129).

 2. Data cannot speak for itself.

Data and visualizations of data created through a method of distant reading do not elucidate anything as such, but require analysis to make them “speak.” As Lisa Gitelman and Virginia Jackson argue, “Data require our participation. Data need us” (6). Moreover, data cannot differentiate between real and spurious correlations. While a search term in Arclight may generate a superfluity of results, without a close analysis of the search term in the context in which it was written, we cannot tell whether its occurrence identifies anything meaningful or not. Data visualizations are informative as an interrogation tool, enabling researchers to see large patterns as well as to detect anomalies, and network diagrams have illustrative value. However, as Drucker discusses in Graphesis: Visual Forms of Knowledge Production, they are only effective if we know how to properly read them, a process that is “essential in our contemporary lives” (4). In this way, she suggests we need to “develop a domain of expertise focused on visual epistemology, knowledge production in graphical form in fields that have rarely relied on visual communication” (6), engage with statistics and critical media studies, understand how to make, manipulate, and comprehend structured data, and create training opportunities for the necessary skill acquisition to undertake digital humanities research (126). Jonathan Gray, researcher in the Department of Media Studies at the University of Amsterdam and Director of Policy and Research at Open Knowledge, takes a similar position in his article “What data can and cannot do,” published in The Guardian. He cautions us not to assume that datasets are readily transparent. Noting the many similarities between official datasets and official texts, he advises us “to learn how to read and interpret them critically, to read between the lines, to notice what is absent or omitted, to understand the gravity and implications of different figures, and so on” (para. 6). As both Drucker and Gray demonstrate, in order to make sense of data we not only require the proper tools but also the refined skills to analyze the data and provide it with a voice.

3. Data cannot be raw.

We need to remember that generating data is a process of interpretation. Since data “needs to be imagined as data to exist and function as such,” this imagining involves a process of interpreting (Gitelman and Jackson 3). In their introduction to “Raw Data” Is an Oxymoron, Gitelman and Jackson expose how perceiving data as “raw” or untouched by social and cultural values and biases “leads to an unnoticed assumption that data are transparent, that information is self-evident, the fundamental stuff of truth itself” (2). Drucker explains how such “realist approaches to visualization assume transparency and equivalence, as if the phenomenal world were self-evident and the apprehension of it a mere mechanical task,” and thus are “fundamentally at odds with approaches to humanities scholarship premised on constructivist principles” (126). Drawing a correlation between photography and data, Gitelman and Jackson argue that just as the “photographic image is always framed, selected out of the profilmic experience in which the photographer stands, points, shoots … [d]ata too need to be understood as framed and framing, understood, that is, according to the use to which they are and can be put” (5).

4. Data cannot be singular or universal.

Despite its common usage as a mass noun and singular verb, data is plural. Gitelman and Jackson explain how “Data’s odd suspension between the singular and the plural reminds us” that data are in fact aggregative (9). Since data pile up, imagining data implicitly involves devising a system of classification (8). In other words, the reduction of phenomena to data entails grouping, dividing, and ranking data, which in turn conceals “ambiguity, conflict, and contradiction” (9). Consequently, we must look “under data to consider their root assumptions” (4). This is not to say that data are not objective, but to understand objectivity as “situated and historically specific; it comes from somewhere and is the result of ongoing changes to the conditions of inquiry, conditions that are at once material, social, and ethical” (4).

 5. Data will not provide us with salvation.

Curated by Olga Subirós and José Luis de Vicente, Big Bang Data is an ongoing project and exhibition that interrogates the current “information explosion” and questions whether data is the new oil. On the Big Bang Data website, Subirós and de Vicente discuss the dangers of data-centrism, including the notion that data can solve all our problems, and stress the importance of retaining certain values, such as subjectivity and ambiguity, “at a time when it is easy to believe that all solutions are computable.” Part of the Big Bang Data project and commissioned by The New York Times, Jonathan Harris’s “Data Will Help Us” is a multi-colored manifesto that also reflects the “promise and perils of data.” In describing the project, Harris exposes how Big Data “has become a kind of ubiquitous modern salve that now gets applied to almost any kind of ailment. In fields ranging from education, to government, to healthcare, to advertising, to dating, to science, to war, we’re abandoning timeless decision-making tools like wisdom, morality, and personal experience for a new kind of logic which simply says: ‘show me the data.’” Projects such as these remind us to be wary of approaching and understanding data as a solution or the answer and reinforce the use of data and digital tools as just one method or tool in our methodological toolboxes.

It is important to understand that what actually constitutes data is controversial. Indeed, Drucker has argued that as a term, data is insufficient, noting its etymological roots in the term “datum,” which translates to “that which is given” (Drucker qtd. in Schöch). In “Humanities Approaches to Graphical Display,” Drucker explains how data “pass themselves off as mere descriptions of a priori conditions,” in turn collapsing the “critical distance between the phenomenal world and its interpretation” (para. 1). In response, she argues that we must “reconceive all data as capta” (para. 3). For Drucker, the term capta captures the “situated, partial, and constitutive character of knowledge production, the recognition that knowledge is constructed, taken, not simply given as a natural representation of pre-existing fact” (para. 3). In “Big? Smart? Clean? Messy? Data in the Humanities,” Christof Schöch observes how even in the absence of a new term, data in DH can be redefined. He suggests the following definition of data as “a digital, selectively constructed, machine-actionable abstraction representing some aspects of a given object of humanistic inquiry.” At a time when data are frequently presented as the ultimate solution, it is important to remain critical. Examining the boundaries and limitations of data reinforce the ways in which data can and cannot be of assistance to us as scholars working with data and employing digital tools in our research.




Works Cited

Big Bang Data. Curated by Olga Subirós and José Luis de Vicente. 2015. Web.

Brooks, David. “What Data Can’t Do.” New York Times. 18 Feb 2013. Web.

Drucker, Johanna. Graphesis: Visual Forms of Knowledge Production. Cambridge: Harvard UP, 2014. Print.

—. “Humanities Approaches to Graphical Display.” Digital Humanities Quarterly 5.1 (2011): n. pag. Web.

Gitelman, Lisa, and Virginia Jackson. “Introduction.” “Raw Data” Is an Oxymoron. Ed. Lisa Gitelman. Cambridge: MIT Press, 2013. 1-14. Print.

Gray, Jonathan. “What data can and cannot do.” The Guardian 31 May 2012. Web.

Harris, Jonathan. “Data Will Help Us.” Big Bang Data. 2013. Web.

Hoyt, Eric. “Teaching with Arclight and POE.” Project Arclight. 12 Oct. 2015. Web.

Owens, Trevor. “Defining Data for Humanists: Text, Artifact, Information or Evidence?” Journal of Digital Humanities 1.1 (2011): n. pag. Web.

Schöch, Christof. “Big? Smart? Clean? Messy? Data in the Humanities.” Journal of Digital Humanities 2.3 (2013): n. pag. Web.



Arclight Software Code Available for Reuse on Github


The code that powers the Arclight web app is available for download and reuse in our Github directory.

None of our software development work would have been possible without the contributions of open source software developers. We were grateful to be able to tweak and use the code of Apache Solr, Blacklight, Ruby on Rails, Javascript, and High Charts, to name just a few software packages and languages that were important to our work. It’s only fitting that we share our code in this same spirit.

The Arclight web app and book are already freely accessible online; now our underlying code is too.

Charles Acland

Charles Acland

A prominent figure in the field of Film and Media Studies, Charles Acland is Professor and Concordia University Research Chair in Communication Studies. Acland has written numerous refereed journal articles (for Film History, Cinema Journal, and Screen, among others), book chapters, and books, and was a past editor of the Canadian Journal of Film Studies. His most recent book, Swift Viewing: The Popular Life of Subliminal Influence, was published in 2012. Acland is also the editor of a number of anthologies, including Useful Cinema (co-edited by Haidee Wasson), which received an Honorable Mention for Best Edited Book in 2013 from the Society for Cinema and Media Studies and was a finalist for the 2012 Kraszna-Krausz Best Moving Image Book Award. For his 2003 book Screen Traffic, Acland was awarded the Gertrude J. Robinson Book Prize for Best Book by a Canadian Communication Scholar. He is regularly invited to present his research nationally and internationally (e.g., Western University, Harvard, and the University of Warwick). As a complement to Useful Cinema, in 2011 Acland used digital technology to extend the impact of this research, developing the Canadian Educational, Sponsored, and Industrial Film (CESIF) Archive at Concordia University. He is the principal investigator of the CESIF project, which includes the construction of an on-line research database (MySQL), containing more than 3,000 entries for largely forgotten films. Additionally, Acland is a Co-Director (with Darren Wershler) of the Media History Research Centre. In 2012, he was the recipient of a SSHRC Insight Grant for his work with “Popular Film and New Media Platforms” and a Québec Government grant (FRQSC) for his research as a co-investigator for ARTHEMIS. Further, for Project Arclight, Acland, the Principal Investigator of the Canadian research team, and Eric Hoyt, the Principal Investigator of the US team, won the 2013 international Digging into Data Challenge Award. For Project Arclight, Acland has established an interdisciplinary team at Concordia with expertise in algorithmic analysis (McKelvey), digital storytelling (Razlogova), and early cinema and database management (Pelletier).

Rob Hunt


Robert Hunt is a second-year master’s student in media studies at Concordia University. His current research analyzes the ways digital media producers manage user data and employ emotional rhetoric as strategies for negotiating the algorithmic control of attention on platforms such as Facebook and Google. Hunt’s role on Project Arclight has primarily been to provide manuscript editing and proofreading during the production of The Arclight Guidebook.

Elise Cotter


Elise Cotter is completing her MA in Media Studies at Concordia University. Her research focuses on the formulation and branding of “Canadian identity.” She is a Research Assistant for the Media History Research Centre and for the CINEMAexpo67 project. She works at Concordia’s MILIEUX Institute as well as being its MHRC Student Representative. Before her masters, Cotter worked at Historica Canada, the largest independent organization devoted to enhancing awareness of Canadian history and citizenship.